Triple

T36746171
Position Surface form Disambiguated ID Type / Status
Subject City of Jumilla E907770 entity
Predicate hasLandmark P105 FINISHED
Object Monastery of Santa Ana del Monte
The Monastery of Santa Ana del Monte is a historic religious complex and pilgrimage site situated on a hill overlooking the town of Jumilla in southeastern Spain.
E2196870 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Monastery of Santa Ana del Monte | Statement: [City of Jumilla, hasLandmark, Monastery of Santa Ana del Monte]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Monastery of Santa Ana del Monte
Triple: [City of Jumilla, hasLandmark, Monastery of Santa Ana del Monte]
Generated description
The Monastery of Santa Ana del Monte is a historic religious complex and pilgrimage site situated on a hill overlooking the town of Jumilla in southeastern Spain.

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69f76e76d10881909ec1679bc043108c completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f7c93ea53481909a5e742cc41adb7e completed May 3, 2026, 10:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3c17335fdc8190b231044eb5670227 completed June 24, 2026, 5:43 p.m.
NEDg Description generation batch_6a3c18778cb481909d7ec4ae70f940bc completed June 24, 2026, 5:48 p.m.
NED2 Entity disambiguation (via description) batch_6a3c53462a408190bb8da22eda8576de completed June 24, 2026, 9:59 p.m.
Created at: May 3, 2026, 4:12 p.m.